GDCRNATools: integrative analysis of protein coding genes, long non-coding genes, and microRNAs in GDC

Ruidong Li and Han Qu

Last update: 30 November, 2017

1 Introduction

GDCRNATools is an R package which provides a standard, easy-to-use and comprehensive pipeline for downloading, organizing, and integrative analyzing RNA expression data in the GDC portal with an emphasis on deciphering the lncRNA-mRNA related ceRNA regulatory network in cancer.

Competing endogenous RNAs (ceRNAs) are RNAs that indirectly regulate other transcripts by competing for shared miRNAs. Although only a fraction of long non-coding RNAs has been functionally characterized, increasing evidences show that lncRNAs harboring multiple miRNA response elements (MREs) can act as ceRNAs to sequester miRNA activity and thus reduce the inhibition of miRNA on its targets. Deregulation of ceRNAs network may lead to human diseases.

The Genomic Data Commons (GDC) maintains standardized genomic, clinical, and biospecimen data from National Cancer Institute (NCI) programs including The Cancer Genome Atlas (TCGA) and Therapeutically Applicable Research To Generate Effective Treatments (TARGET), It also accepts high quality datasets from non-NCI supported cancer research programs, such as genomic data from the Foundation Medicine.

Many analyses can be perfomed using GDCRNATools, including differential gene expression analysis (limma(Ritchie et al. 2015), edgeR(Robinson, McCarthy, and Smyth 2010), and DESeq2(Love, Huber, and Anders 2014)), univariate survival analysis (CoxPH and KM), competing endogenous RNA network analysis (hypergeometric test, Pearson correlation analysis, regulation similarity analysis, sensitivity Pearson partial correlation(Paci, Colombo, and Farina 2014)), and functional enrichment analysis(GO, KEGG, DO). Besides some routine visualization methods such as volcano plot, scatter plot, and bubble plot, etc., three simple shiny apps are developed in GDCRNATools allowing users visualize the results on a local webpage. All the figures are plotted based on ggplot2 package unless otherwise specified.

This user-friendly package allows researchers perform the analysis by simply running a few functions and integrate their own pipelines such as molecular subtype classification, weighted correlation network analysis (WGCNA)(Langfelder and Horvath 2008), and TF-miRNA co-regulatory network analysis, etc. into the workflow easily. This could open a door to accelerate the study of crosstalk among different classes of RNAs and their regulatory relationships in cancer.

2 GDCRNATools package installation

The R software for running GDCRNATools can be downloaded from The Comprehensive R Archive Network (CRAN). The GDCRNATools package can be installed from Github.

devtools::install_github(repo='Jialab-UCR/GDCRNATools')
library(GDCRNATools)

3 Data download

Two methods are provided for downloading Gene Expression Quantification (HTSeq-Counts), Isoform Expression Quantification (BCGSC miRNA Profiling), and Clinical (Clinical Supplement) data:

  • Manual download
    Step1: Download GDC Data Transfer Tool on the GDC website
    Step2: Add data to the GDC cart, then download manifest file and metadata of the cart
    Step3: Download data using gdcRNADownload() function by providing the manifest file

  • Automatic download
    Download GDC Data Transfer Tool, manifest file, and data automatically by specifying the project.id and data.type in gdcRNADownload() function for RNAseq and miRNAs data, and in gdcClinicalDownload() function for clinical data

Users can also download data from GDC using the API method developed in TCGAbiolinks(Colaprico et al. 2016) or using TCGA-Assembler(Zhu, Qiu, and Ji 2014)

3.1 Manual download

3.1.1 Installation of GDC Data Transfer Tool gdc-client

Download GDC Data Transfer Tool from the GDC website and unzip the file

3.1.2 Download manifest file and metadata from GDC Data Portal

3.1.3 Download data

####### Download RNAseq data #######
gdcRNADownload(manifest  = 'TCGA-PRAD/TCGA-PRAD.RNAseq.gdc_manifest.2017-11-23T14-40-52.txt',
               directory = 'TCGA-PRAD/RNAseq')

####### Download miRNAs data #######
gdcRNADownload(manifest  = 'TCGA-PRAD/TCGA-PRAD.miRNAs.gdc_manifest.2017-11-22T15-36-57.txt',
               directory = 'TCGA-PRAD/miRNAs')

####### Download Clinical data #######
gdcRNADownload(manifest  = 'TCGA-PRAD/TCGA-PRAD.Clinical.gdc_manifest.2017-11-23T14-42-01.txt',
               directory = 'TCGA-PRAD/Clinical')

3.2 Automatic download

  • gdcRNADownload() will download HTSeq-Counts data if data.type='RNAseq' and download BCGSC miRNA Profiling data if data.type='miRNAs'. project.id argument is required to be provided.

  • gdcClinicalDownload() download clinical data in .xml format automatically by simply specifying the project.id argument.

3.2.1 Download RNAseq/miRNAs data

####### Download RNAseq data #######
gdcRNADownload(project.id     = 'TCGA-PRAD', 
               data.type      = 'RNAseq', 
               write.manifest = TRUE,
               directory      = 'TCGA-PRAD/RNAseq')

####### Download miRNAs data #######
gdcRNADownload(project.id     = 'TCGA-PRAD', 
               data.type      = 'miRNAs', 
               write.manifest = TRUE,
               directory      = 'TCGA-PRAD/miRNAs')

3.2.2 Download clinical data

####### Download clinical data #######
gdcClinicalDownload(project.id     = 'TCGA-PRAD', 
                    data.type      = 'RNAseq', 
                    write.manifest = TRUE,
                    directory      = 'TCGA-PRAD/Clinical')

4 Data organization

4.1 Parse metadata

Metadata can be parsed by either providing the metadata file that is downloaded in the data download step, or specifying the project.id and data.type in gdcParseMetadata() function to obtain information of data in the manifest file to facilitate data organization and basic clinical information of patients such as age, stage and gender, etc. for data analysis.

4.1.1 Parse metadata by providing the metadata file

####### Parse RNAseq metadata #######
metaMatrix.RNA <- gdcParseMetadata(metafile='TCGA-PRAD/TCGA-PRAD.RNAseq.metadata.2017-11-23T17-23-59.json')

####### Parse miRNAs metadata #######
metaMatrix.MIR <- gdcParseMetadata(metafile='TCGA-PRAD/TCGA-PRAD.miRNAs.metadata.2017-11-23T17-33-55.json')

4.1.2 Parse metadata by specifying project.id and data.type

####### Parse RNAseq metadata #######
metaMatrix.RNA <- gdcParseMetadata(project.id = 'TCGA-PRAD',
                                   data.type  = 'RNAseq', 
                                   write.meta = TRUE)

metaMatrix.RNA[1:6,1:6]
##                                                             file_name
## TCGA-2A-A8VL-01A d5b5e519-5ce4-4147-a500-25b2f442152d.htseq.counts.gz
## TCGA-2A-A8VO-01A ed22ecf9-2215-4bc4-a660-f8dbd2e2d15c.htseq.counts.gz
## TCGA-2A-A8VT-01A 4dd0008c-f544-438d-8802-e02dbf6c4a3e.htseq.counts.gz
## TCGA-2A-A8VV-01A 8f92f24b-90c7-46d0-b69d-e1d87a135a40.htseq.counts.gz
## TCGA-2A-A8VX-01A 6f421ec2-c74a-4719-b447-fab39c619d3b.htseq.counts.gz
## TCGA-2A-A8W1-01A 5d408d1f-e5e4-4901-b1a8-bf803e633117.htseq.counts.gz
##                                               file_id      patient
## TCGA-2A-A8VL-01A 2b760030-1cad-4554-931e-bac9205b56ca TCGA-2A-A8VL
## TCGA-2A-A8VO-01A 8c4da184-f1e1-4439-b501-8c3f88ba0d23 TCGA-2A-A8VO
## TCGA-2A-A8VT-01A 32eb6261-4d8b-4f8e-9435-cdbdc4766a3d TCGA-2A-A8VT
## TCGA-2A-A8VV-01A 8a880dc3-c706-4a16-9e1a-39adfdb7c72a TCGA-2A-A8VV
## TCGA-2A-A8VX-01A 2bacf214-6dfb-44e3-b5ab-ec51c343b24e TCGA-2A-A8VX
## TCGA-2A-A8W1-01A f6c810f2-344e-4e8e-8e7a-01c18ec72074 TCGA-2A-A8W1
##                           sample     submitter_id
## TCGA-2A-A8VL-01A TCGA-2A-A8VL-01 TCGA-2A-A8VL-01A
## TCGA-2A-A8VO-01A TCGA-2A-A8VO-01 TCGA-2A-A8VO-01A
## TCGA-2A-A8VT-01A TCGA-2A-A8VT-01 TCGA-2A-A8VT-01A
## TCGA-2A-A8VV-01A TCGA-2A-A8VV-01 TCGA-2A-A8VV-01A
## TCGA-2A-A8VX-01A TCGA-2A-A8VX-01 TCGA-2A-A8VX-01A
## TCGA-2A-A8W1-01A TCGA-2A-A8W1-01 TCGA-2A-A8W1-01A
##                           entity_submitter_id
## TCGA-2A-A8VL-01A TCGA-2A-A8VL-01A-21R-A37L-07
## TCGA-2A-A8VO-01A TCGA-2A-A8VO-01A-11R-A37L-07
## TCGA-2A-A8VT-01A TCGA-2A-A8VT-01A-11R-A37L-07
## TCGA-2A-A8VV-01A TCGA-2A-A8VV-01A-11R-A37L-07
## TCGA-2A-A8VX-01A TCGA-2A-A8VX-01A-11R-A37L-07
## TCGA-2A-A8W1-01A TCGA-2A-A8W1-01A-11R-A37L-07
####### Parse miRNAs metadata #######
metaMatrix.MIR <- gdcParseMetadata(project.id = 'TCGA-PRAD',
                                   data.type  = 'miRNAs', 
                                   write.meta = TRUE)
metaMatrix.MIR[1:6,1:6]
##                                                                                   file_name
## TCGA-2A-A8VL-01A a8ad4b62-68e8-4d56-893e-e247a3099d94.mirbase21.isoforms.quantification.txt
## TCGA-2A-A8VO-01A 22302d39-da19-4bfd-b4d8-aa951b9451a1.mirbase21.isoforms.quantification.txt
## TCGA-2A-A8VT-01A de5cc4c2-2709-4bbe-8777-9d8e9cd56246.mirbase21.isoforms.quantification.txt
## TCGA-2A-A8VV-01A f3402505-e2c1-4720-a9f2-f39105ad0327.mirbase21.isoforms.quantification.txt
## TCGA-2A-A8VX-01A c2a2d423-e481-4821-9970-5e93d7d4442b.mirbase21.isoforms.quantification.txt
## TCGA-2A-A8W1-01A b1a5f1a4-a95a-4770-a234-709c4e9da1fe.mirbase21.isoforms.quantification.txt
##                                               file_id      patient
## TCGA-2A-A8VL-01A a0b6cbc1-43fa-4bed-83e8-917794158b98 TCGA-2A-A8VL
## TCGA-2A-A8VO-01A addea5e5-5b25-417c-bbb2-00438b8da4c6 TCGA-2A-A8VO
## TCGA-2A-A8VT-01A 7a337162-08ee-4600-96f5-79fed7b68898 TCGA-2A-A8VT
## TCGA-2A-A8VV-01A fc64fdd9-b679-4a97-bf5e-d757b64b252c TCGA-2A-A8VV
## TCGA-2A-A8VX-01A 8387f768-8d31-4ffa-88ae-dae0ef11b2fb TCGA-2A-A8VX
## TCGA-2A-A8W1-01A cb18f79c-41d4-4bb9-af6a-28e35b6a4470 TCGA-2A-A8W1
##                           sample     submitter_id
## TCGA-2A-A8VL-01A TCGA-2A-A8VL-01 TCGA-2A-A8VL-01A
## TCGA-2A-A8VO-01A TCGA-2A-A8VO-01 TCGA-2A-A8VO-01A
## TCGA-2A-A8VT-01A TCGA-2A-A8VT-01 TCGA-2A-A8VT-01A
## TCGA-2A-A8VV-01A TCGA-2A-A8VV-01 TCGA-2A-A8VV-01A
## TCGA-2A-A8VX-01A TCGA-2A-A8VX-01 TCGA-2A-A8VX-01A
## TCGA-2A-A8W1-01A TCGA-2A-A8W1-01 TCGA-2A-A8W1-01A
##                           entity_submitter_id
## TCGA-2A-A8VL-01A TCGA-2A-A8VL-01A-21R-A37H-13
## TCGA-2A-A8VO-01A TCGA-2A-A8VO-01A-11R-A37H-13
## TCGA-2A-A8VT-01A TCGA-2A-A8VT-01A-11R-A37H-13
## TCGA-2A-A8VV-01A TCGA-2A-A8VV-01A-11R-A37H-13
## TCGA-2A-A8VX-01A TCGA-2A-A8VX-01A-11R-A37H-13
## TCGA-2A-A8W1-01A TCGA-2A-A8W1-01A-11R-A37H-13

4.2 Filter samples

4.2.1 Filter duplicated samples

Only one sample would be kept if the sample had been sequenced more than once by gdcFilterDuplicate().

####### Filter duplicated samples in RNAseq metadata #######
metaMatrix.RNA <- gdcFilterDuplicate(metaMatrix.RNA)
## Removed 3 samples
####### Filter duplicated samples in miRNAs metadata #######
metaMatrix.MIR <- gdcFilterDuplicate(metaMatrix.MIR)
## Removed 4 samples

4.2.2 Filter non-Primary Tumor and non-Solid Tissue Normal samples

Samples that are neither Primary Tumor (code: 01) nor Solid Tissue Normal (code: 11) would be filtered out by gdcFilterSampleType().

####### Filter non-Primary Tumor and non-Solid Tissue Normal samples in RNAseq metadata #######
metaMatrix.RNA <- gdcFilterSampleType(metaMatrix.RNA)
## Removed 1 samples
####### Filter non-Primary Tumor and non-Solid Tissue Normal samples in miRNAs metadata #######
metaMatrix.MIR <- gdcFilterSampleType(metaMatrix.MIR)
## Removed 1 samples

4.3 Merge data

  • gdcRNAMerge() merges raw counts data of RNAseq to a single expression matrix with rows are Ensembl id and columns are samples. Total read counts for 5p and 3p strands of miRNAs can be processed from isoform quantification files and then merged to a single expression matrix with rows are miRBase v21 identifiers and columns are samples.

  • gdcClinicalMerge() merges clinical data to a dataframe with rows are patient id and columns are clinical traits. If key.info=TRUE, only those most commonly used clinical traits will be reported, otherwise, all the clinical information will be reported.

4.3.1 Merge RNAseq/miRNAs data

####### Merge RNAseq data #######
rnaMatrix <- gdcRNAMerge(metadata  = metaMatrix.RNA, 
                         path      = 'TCGA-PRAD/RNAseq/', 
                         data.type = 'RNAseq')
## ############### Merging RNAseq data ################
## ### This step may take a few minutes ###
## Number of samples: 547
## Number of genes: 60483
rnaMatrix[1:6,1:6]
##                 TCGA-2A-A8VL-01 TCGA-2A-A8VO-01 TCGA-2A-A8VT-01
## ENSG00000000003            2867            1667            3140
## ENSG00000000005               6               0               0
## ENSG00000000419            1354             888            1767
## ENSG00000000457             956             580            2163
## ENSG00000000460             119              91             305
## ENSG00000000938             159             171             228
##                 TCGA-2A-A8VV-01 TCGA-2A-A8VX-01 TCGA-2A-A8W1-01
## ENSG00000000003            3996            4869            2172
## ENSG00000000005              44               1               0
## ENSG00000000419            1408            1171            1593
## ENSG00000000457            1494             908             794
## ENSG00000000460             175             121             166
## ENSG00000000938             172              64             161
####### Merge miRNAs data #######
mirMatrix <- gdcRNAMerge(metadata  = metaMatrix.MIR,
                         path      = 'TCGA-PRAD/miRNAs/',
                         data.type = 'miRNAs')
## ############### Merging miRNAs data ###############
## Number of samples: 546
## Number of miRNAs: 2588
mirMatrix[1:6,1:6]
##                 TCGA-2A-A8VL-01 TCGA-2A-A8VO-01 TCGA-2A-A8VT-01
## hsa-let-7a-5p            130022           77195          170937
## hsa-let-7a-3p               133              84              91
## hsa-let-7a-2-3p              18              10              13
## hsa-let-7b-5p             68276           19131           36009
## hsa-let-7b-3p                78              30              55
## hsa-let-7c-5p             43015           22490           14099
##                 TCGA-2A-A8VV-01 TCGA-2A-A8VX-01 TCGA-2A-A8W1-01
## hsa-let-7a-5p            247370           73705           50261
## hsa-let-7a-3p               104              59              39
## hsa-let-7a-2-3p              13               3               4
## hsa-let-7b-5p             58349           17404            6663
## hsa-let-7b-3p                73              19              18
## hsa-let-7c-5p             36248            9694           11759

4.3.2 Merge clinical data

####### Merge clinical data #######
clinicalDa <- gdcClinicalMerge(path = 'TCGA-PRAD/Clinical/', key.info = TRUE)
## ############### Merging Clinical data ###############
clinicalDa[1:6,5:10]
##              clinical_stage clinical_T clinical_N clinical_M
## TCGA-EJ-5510             NA        T1c         NA         M0
## TCGA-HC-8260             NA         NA         NA         M0
## TCGA-Y6-A8TL             NA        T2a         NA         NA
## TCGA-V1-A8X3             NA        T1c         NA         M0
## TCGA-VP-A87J             NA        T2a         NA         M0
## TCGA-KK-A6DY             NA        T1c         NA         M0
##              gleason_grading gleason_score
## TCGA-EJ-5510            7433             7
## TCGA-HC-8260             734             7
## TCGA-Y6-A8TL             633             6
## TCGA-V1-A8X3             734             7
## TCGA-VP-A87J             734             7
## TCGA-KK-A6DY             734             7

4.4 TMM normalization and voom transformation

It has repeatedly shown that normalization is a critical way to ensure accurate estimation and detection of differential expression (DE) by removing systematic technical effects that occur in the data(Robinson and Oshlack 2010). TMM normalization is a simple and effective method for estimating relative RNA production levels from RNA-seq data. Voom is moreover faster and more convenient than existing RNA-seq methods, and converts RNA-seq data into a form that can be analyzed using similar tools as for microarrays(Law et al. 2014).

By running gdcVoomNormalization() function, raw counts data would be normalized by TMM method implemented in edgeR(Robinson, McCarthy, and Smyth 2010) and further transformed by the voom method provided in limma(Ritchie et al. 2015). Low expression genes (logcpm < 1 in more than half of the samples) will be filtered out by default. All the genes can be kept by setting filter=TRUE in the gdcVoomNormalization().

####### RNAseq data #######
rnaExpr <- gdcVoomNormalization(counts = rnaMatrix, filter = FALSE)
rnaExpr[1:6,1:6]
##                 TCGA-2A-A8VL-01 TCGA-2A-A8VO-01 TCGA-2A-A8VT-01
## ENSG00000000003        5.891004        5.469541        5.675430
## ENSG00000000005       -2.894134       -6.233930       -6.941348
## ENSG00000000419        4.808971        4.561298        4.846146
## ENSG00000000457        4.307047        3.947222        5.137803
## ENSG00000000460        1.306293        1.281770        2.313680
## ENSG00000000938        1.722839        2.188135        1.894702
##                 TCGA-2A-A8VV-01 TCGA-2A-A8VX-01 TCGA-2A-A8W1-01
## ENSG00000000003       6.3329382       6.6613451        5.612615
## ENSG00000000005      -0.1558497      -5.0032503       -6.472525
## ENSG00000000419       4.8283607       4.6059284        5.165458
## ENSG00000000457       4.9138640       4.2391299        4.161378
## ENSG00000000460       1.8237441       1.3365997        1.906853
## ENSG00000000938       1.7988694       0.4230145        1.862865
####### miRNAs data #######
mirExpr <- gdcVoomNormalization(counts = mirMatrix, filter = FALSE)
mirExpr[1:6,1:6]
##                 TCGA-2A-A8VL-01 TCGA-2A-A8VO-01 TCGA-2A-A8VT-01
## hsa-let-7a-5p         14.676762       14.246607       15.773276
## hsa-let-7a-3p          4.749056        4.411257        4.905866
## hsa-let-7a-2-3p        1.897814        1.402695        2.145054
## hsa-let-7b-5p         13.747462       12.234040       13.526256
## hsa-let-7b-3p          3.982981        2.941115        4.184582
## hsa-let-7c-5p         13.080929       12.467406       12.173523
##                 TCGA-2A-A8VV-01 TCGA-2A-A8VX-01 TCGA-2A-A8W1-01
## hsa-let-7a-5p         15.705812      14.8712423       14.456561
## hsa-let-7a-3p          4.496858       4.5965754        4.143175
## hsa-let-7a-2-3p        1.544386       0.5091125        1.009320
## hsa-let-7b-5p         13.621931      12.7889304       11.541459
## hsa-let-7b-3p          3.989171       2.9871598        3.048848
## hsa-let-7c-5p         12.935132      11.9447084       12.360934

5 Differential gene expression analysis


gdcDEAnalysis(), a convenience wrapper, provides three widely used methods limma(Ritchie et al. 2015), edgeR(Robinson, McCarthy, and Smyth 2010), and DESeq2(Love, Huber, and Anders 2014) to identify differentially expressed genes (DEGs) or miRNAs between any two groups defined by users. Note that DESeq2(Love, Huber, and Anders 2014) maybe slow with a single core. Multiple cores can be specified with the nCore argument if DESeq2(Love, Huber, and Anders 2014) is in use. Users are encouraged to consult the vignette of each method for more detailed information.

5.1 DE analysis

DEGAll <- gdcDEAnalysis(counts     = rnaMatrix, 
                        group      = metaMatrix.RNA$sample_type, 
                        comparison = 'PrimaryTumor-SolidTissueNormal', 
                        method     = 'limma')
DEGAll[1:6,]
##                  symbol          group     logFC   AveExpr         t
## ENSG00000187699 C2orf88 protein_coding -2.657180 1.5056478 -19.46636
## ENSG00000176928   GCNT4 protein_coding -2.248112 0.5798701 -18.39206
## ENSG00000118298    CA14 protein_coding -2.630802 0.4748363 -17.57925
## ENSG00000103485    QPRT protein_coding -2.147259 1.9897483 -17.32704
## ENSG00000109667  SLC2A9 protein_coding -1.869863 1.6079446 -17.21612
## ENSG00000164764  SBSPON protein_coding -2.333725 2.5270242 -17.17468
##                       PValue          FDR        B
## ENSG00000187699 1.453473e-64 2.259715e-60 136.1299
## ENSG00000176928 3.303402e-59 2.567900e-55 123.6779
## ENSG00000118298 3.348976e-55 1.735551e-51 114.6210
## ENSG00000103485 5.729814e-54 2.227035e-50 111.9647
## ENSG00000109667 1.990189e-53 6.188294e-50 110.6756
## ENSG00000164764 3.166847e-53 8.205828e-50 110.2957

5.2 Report DE genes/miRNAs

All DEGs, DE long non-coding genes, DE protein coding genes and DE miRNAs could be reported separately by setting geneType argument in gdcDEReport(). Gene symbols and biotypes based on the Ensembl 90 annotation are reported in the output.

### All DEGs
deALL <- gdcDEReport(deg = DEGAll, gene.type = 'all')

#### DE long-noncoding
deLNC <- gdcDEReport(deg = DEGAll, gene.type = 'long_non_coding')

#### DE protein coding genes
dePC <- gdcDEReport(deg = DEGAll, gene.type = 'protein_coding')

5.3 DEG visualization

Volcano plot and bar plot are used to visualize DE analysis results in different manners by gdcVolcanoPlot() and gdcBarPlot() functions, respectively . Hierarchical clustering on the expression matrix of DEGs can be analyzed and plotted by the gdcHeatmap() function.

5.3.1 Volcano plot

gdcVolcanoPlot(DEGAll)

5.3.2 Barplot

gdcBarPlot(deg = deALL, angle = 45, data.type = 'RNAseq')

5.3.3 Heatmap

Heatmap is generated based on the heatmap.2() function in gplots package.

degName = rownames(deALL)
gdcHeatmap(deg.id = degName, metadata = metaMatrix.RNA, rna.expr = rnaExpr)

6 Competing endogenous RNAs network analysis

Three criteria are used to determine the competing endogenous interactions between lncRNA-mRNA pairs:

  • The lncRNA and mRNA must share significant number of miRNAs
  • Expression of lncRNA and mRNA must be positively correlated
  • Those common miRNAs should play similar roles in regulating the expression of lncRNA and mRNA

6.1 Hypergeometric test

Hypergenometric test is performed to test whether a lncRNA and mRNA share many miRNAs significantly.

A newly developed algorithm spongeScan(Furi’o-Tar’i et al. 2016) is used to predict MREs in lncRNAs acting as ceRNAs. Databases such as starBase v2.0(J.-H. Li et al. 2014), miRcode(Jeggari, Marks, and Larsson 2012) and mirTarBase release 7.0(Chou et al. 2017) are used to collect predicted and experimentally validated miRNA-mRNA and/or miRNA-lncRNA interactions. Gene IDs in these databases are updated to the latest Ensembl 90 annotation of human genome and miRNAs names are updated to the new release miRBase 21 identifiers. Users can also provide their own datasets of miRNA-lncRNA and miRNA-mRNA interactions.

The figure and equation below illustrate how the hypergeometric test works

\[p=1-\sum_{k=0}^m \frac{\binom{K}{k}\binom{N-K}{n-k}}{\binom{N}{n}} \] here \(m\) is the number of shared miRNAs, \(N\) is the total number of miRNAs in the database, \(n\) is the number of miRNAs targeting the lncRNA, \(K\) is the number of miRNAs targeting the protein coding gene.

6.2 Pearson correlation analysis

Pearson correlation coefficient is a measure of the strength of a linear association between two variables. As we all know, miRNAs are negative regulators of gene expression. If more common miRNAs are occupied by a lncRNA, less of them will bind to the target mRNA, thus increasing the expression level of mRNA. So expression of the lncRNA and mRNA in a ceRNA pair should be positively correlated.

6.3 Regulation pattern analysis

Two methods are used to measure the regulatory role of miRNAs on the lncRNA and mRNA:

  • Regulation similarity

We defined a measurement regulation similarity score to check the similarity between miRNAs-lncRNA expression correlation and miRNAs-mRNA expression correlation.

\[Regulation\ similarity\ score = 1-\frac{1}{M} \sum_{k=1}^M [{\frac{|corr(m_k,l)-corr(m_k,g)|}{|corr(m_k,l)|+|corr(m_k,g)|}}]^M\]

where \(M\) is the total number of shared miRNAs, \(k\) is the \(k\)th shared miRNAs, \(corr(m_k, l)\) and \(corr(m_k, g)\) represents the Pearson correlation between the \(k\)th miRNA and lncRNA, the \(k\)th miRNA and mRNA, respectively

  • Sensitivity correlation

Sensitivity correlation is defined by Paci et al.(Paci, Colombo, and Farina 2014) to measure if the correlation between a lncRNA and mRNA is mediated by a miRNA in the lncRNA-miRNA-mRNA triplet. We take average of all triplets of a lncRNA-mRNA pair and their shared miRNAs as the sensitivity correlation between a selected lncRNA and mRNA.

\[Sensitivity\ correlation = corr(l,g)-\frac{1}{M}\sum_{k=1}^M {\frac{corr(l,g)-corr(m_k,l)corr(m_k,g)}{\sqrt{1-corr(m_k,l)^2}\sqrt{1-corr(m_k,g)^2}}}\] where \(M\) is the total number of shared miRNAs, \(k\) is the \(k\)th shared miRNAs, \(corr(l,g)\), \(corr(m_k,l)\) and \(corr(m_k, g)\) represents the Pearson correlation between the long non-coding RNA and the protein coding gene, the kth miRNA and lncRNA, the kth miRNA and mRNA, respectively


The hypergeometric test of shared miRNAs, expression correlation analysis of lncRNA-mRNA pair, and regulation pattern analysis of shared miRNAs are all implemented in the gdcCEAnalysis() function.

ceOutput <- gdcCEAnalysis(lnc         = rownames(deLNC), 
                          pc          = rownames(dePC), 
                          lnc.targets = 'starBase', 
                          pc.targets  = 'starBase', 
                          rna.expr    = rnaExpr, 
                          mir.expr    = mirExpr)
## Step 1/3: Hypergenometric test done !
## Step 2/3: Correlation analysis done !
## Step 3/3: Regulation pattern analysis done !
ceOutput <- ceOutput[order(ceOutput$regSim),]
ceOutput[1:6,]
##            lncRNAs           Genes Counts listTotal popHits popTotal
## 22 ENSG00000234456 ENSG00000163110      2         2      36      277
## 34 ENSG00000234456 ENSG00000043591      2         2      51      277
## 37 ENSG00000234456 ENSG00000119547      2         2      71      277
## 45 ENSG00000234456 ENSG00000112984      2         2       3      277
## 57 ENSG00000234456 ENSG00000184838      2         2      23      277
## 65 ENSG00000228223 ENSG00000129514      1         2      23      277
##      foldEnrichment          hyperPValue                          miRNAs
## 22 7.69444444444444    0.016480929210485 hsa-miR-374b-5p,hsa-miR-374a-5p
## 34 5.43137254901961   0.0333542614974101 hsa-miR-374b-5p,hsa-miR-374a-5p
## 37 3.90140845070423   0.0650081096635797 hsa-miR-374b-5p,hsa-miR-374a-5p
## 45 92.3333333333333 7.84806152880239e-05 hsa-miR-374b-5p,hsa-miR-374a-5p
## 57 12.0434782608696  0.00661853188929001 hsa-miR-374b-5p,hsa-miR-374a-5p
## 65 6.02173913043478    0.159446450060168                  hsa-miR-590-3p
##           cor corPValue regSim          sppc
## 22 -0.3384664 1.0000000      0  0.0006305722
## 34 -0.4330765 1.0000000      0  0.0012113510
## 37 -0.3662249 1.0000000      0 -0.0241567080
## 45 -0.4187818 1.0000000      0 -0.0297453228
## 57 -0.2210811 0.9999999      0 -0.0155415582
## 65 -0.3084449 1.0000000      0 -0.0061604421

6.4 ceRNAs visualization

6.4.1 Correlation plot

gdcCorPlot(gene1    = 'ENSG00000234456', 
           gene2    = 'ENSG00000105971',
           rna.expr = rnaExpr,
           metadata = metaMatrix.RNA)

6.4.2 Correlation plot on a local webpage by shinyCorplot

Typing and running gdcCorPlot() for each pair of lncRNA-mRNA is bothering when multiple pairs are being interested in. shinyCorPlot() , a interactive plot function based on shiny package, can be easily operated by just clicking the genes in each drop down box (in the GUI window). By running shinyCorPlot() function, a local webpage would pop up and correlation plot between a lncRNA and mRNA would be automatically shown.

shinyCorPlot(gene1    = rownames(deLNC), 
             gene2    = rownames(dePC), 
             rna.expr = rnaExpr, 
             metadata = metaMatrix.RNA)

6.4.3 Network visulization in Cytoscape

lncRNA-miRNA-mRNA interactions can be reported by the gdcExportNetwork() and visualized in Cytoscape.

ceOutput2 <- ceOutput[ceOutput$hyperPValue<0.01 & ceOutput$corPValue<0.01 & ceOutput$regSim != 0,]

edges <- gdcExportNetwork(ceNetwork = ceOutput2, net = 'edges')
edges[1:6,]
##          fromNode          toNode altNode1Name
## 1 ENSG00000234456 hsa-miR-374b-5p    MAGI2-AS3
## 2 ENSG00000234456 hsa-miR-374a-5p    MAGI2-AS3
## 3 ENSG00000245532   hsa-let-7i-5p        NEAT1
## 4 ENSG00000245532   hsa-let-7e-5p        NEAT1
## 5 ENSG00000245532   hsa-let-7g-5p        NEAT1
## 6 ENSG00000245532   hsa-let-7f-5p        NEAT1
nodes <- gdcExportNetwork(ceNetwork = ceOutput2, net = 'nodes')
nodes[1:6,]
##              gene symbol type numInteractions
## 1 ENSG00000008300 CELSR3   pc               8
## 2 ENSG00000047597     XK   pc               2
## 3 ENSG00000065320   NTN1   pc               2
## 4 ENSG00000065534   MYLK   pc               2
## 5 ENSG00000066468  FGFR2   pc               2
## 6 ENSG00000075651   PLD1   pc               1

7 Univariate survival analysis

Two methods are provided to perform univariate survival analysis: Cox Proportional-Hazards (CoxPH) model and Kaplan Meier (KM) analysis based on the survival package. CoxPH model considers expression value as continous variable while KM analysis divides patients into high-expreesion and low-expression groups by a user-defined threshold such as median or mean. gdcSurvivalAnalysis() take a list of genes as input and report the hazard ratio, 95% confidence intervals, and test significance of each gene on overall survival.

7.1 CoxPH analysis

####### CoxPH analysis #######
survOutput <- gdcSurvivalAnalysis(gene     = rownames(deALL), 
                                  method   = 'coxph', 
                                  rna.expr = rnaExpr, 
                                  metadata = metaMatrix.RNA)

head(survOutput[order(survOutput$pValue),])
##                     symbol       coef        HR   lower95    upper95
## ENSG00000156804     FBXO32 -0.9061689 0.4040693 0.2444365  0.6679526
## ENSG00000273478 AC099676.1  1.8426288 6.3131126 1.9864365 20.0637629
## ENSG00000069535       MAOB -0.4870443 0.6144398 0.4517982  0.8356304
## ENSG00000128298   BAIAP2L2  0.4950804 1.6406302 1.1837845  2.2737816
## ENSG00000255545 AP004608.1  0.7108727 2.0357671 1.2702956  3.2625066
## ENSG00000180447       GAS1 -0.6253213 0.5350895 0.3530306  0.8110367
##                       pValue
## ENSG00000156804 0.0004100575
## ENSG00000273478 0.0017880566
## ENSG00000069535 0.0019053414
## ENSG00000128298 0.0029472842
## ENSG00000255545 0.0031344643
## ENSG00000180447 0.0032084588

7.2 KM analysis

####### KM analysis #######
survOutput <- gdcSurvivalAnalysis(gene     = rownames(deALL), 
                                  method   = 'KM', 
                                  rna.expr = rnaExpr, 
                                  metadata = metaMatrix.RNA, 
                                  sep      = 'median')

7.3 KM analysis visualization

7.3.1 KM plot

KM survival curves are ploted using the gdcKMPlot() function which is based on the R package survminer.

gdcKMPlot(gene     = 'ENSG00000251321', 
          rna.expr = rnaExpr, 
          metadata = metaMatrix.RNA, 
          sep      = 'median')

7.3.2 KM plot on a local webpage by shinyKMPlot

The shinyKMPlot() function is also a simply shiny app which allow users view KM plots of all genes of interests on a local webpackage conveniently.

shinyKMPlot(gene = rownames(deALL), rna.expr = rnaExpr, metadata = metaMatrix.RNA)

8 Functional enrichment analysis

One of the main uses of the GO is to perform enrichment analysis on gene sets. For example, given a set of genes that are up-regulated under certain conditions, an enrichment analysis will find which GO terms are over-represented (or under-represented) using annotations for that gene set and pathway enrichment can also be applied afterwards.


8.1 GO, KEGG and DO analyses

gdcEnrichAnalysis() can perform Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Disease Ontology (DO) functional enrichment analyses of a list of genes simultaneously. GO and KEGG analyses are based on the R/Bioconductor packages clusterProfilier(Yu et al. 2012) and DOSE(Yu et al. 2015). Redundant GO terms can be removed by specifying simplify=TRUE in the gdcEnrichAnalysis() function which uses the simplify() function in the clusterProfilier(Yu et al. 2012) package.

enrichOutput <- gdcEnrichAnalysis(gene = rownames(deALL), simplify = TRUE)
## ### This step may take a few minutes ###
## Step 1/5: BP analysis done!
## Step 2/5: CC analysis done!
## Step 3/5: MF analysis done!
## Step 4/5: KEGG analysis done!
## Step 5/5: DO analysis done!
terms <- c()
for (category in c('GO_BP', 'GO_CC', 'GO_MF', 'KEGG', 'DO')) {
  terms <- c(terms, which(enrichOutput$Category==category)[1:3])
}

enrichOutput[terms,]
##                                                               Terms Counts
## 1                                     GO:0006936~muscle contraction     77
## 2                      GO:2000027~regulation of organ morphogenesis     47
## 3                   GO:0051146~striated muscle cell differentiation     56
## 63                                  GO:0031012~extracellular matrix     91
## 64                                     GO:0043292~contractile fiber     55
## 65                                            GO:0042383~sarcolemma     38
## 79                             GO:0005539~glycosaminoglycan binding     43
## 80                                      GO:0015267~channel activity     71
## 81            GO:0022803~passive transmembrane transporter activity     71
## 91                            hsa05414~Dilated cardiomyopathy (DCM)     25
## 92                       hsa05410~Hypertrophic cardiomyopathy (HCM)     22
## 93  hsa05412~Arrhythmogenic right ventricular cardiomyopathy (ARVC)     20
## 101                                      DOID:10283~prostate cancer     76
## 102                        DOID:3856~male reproductive organ cancer     76
## 103                                               DOID:423~myopathy     69
##     GeneRatio   BgRatio       pValue          FDR foldEnrichment
## 1     77/1353 326/16447 9.881094e-18 4.882249e-14       2.871191
## 2     47/1353 185/16447 1.527429e-12 2.515676e-09       3.088268
## 3     56/1353 249/16447 2.588835e-12 2.741716e-09       2.733868
## 63    91/1431 425/17563 5.476425e-18 2.650589e-15       2.627916
## 64    55/1431 217/17563 1.472759e-14 2.376051e-12       3.110728
## 65    38/1431 124/17563 3.068355e-13 3.320284e-11       3.761153
## 79    43/1351 200/16514 3.287862e-09 2.689471e-06       2.628061
## 80    71/1351 450/16514 5.409995e-08 1.475125e-05       1.928603
## 81    71/1351 450/16514 5.409995e-08 1.475125e-05       1.928603
## 91     25/607   89/7174 4.416347e-08 1.245410e-05       3.319882
## 92     22/607   83/7174 8.615268e-07 1.139528e-04       3.132689
## 93     20/607   72/7174 1.212263e-06 1.139528e-04       3.282995
## 101    76/842  412/7577 3.902626e-06 2.993314e-03       1.659975
## 102    76/842  422/7577 9.701641e-06 3.720579e-03       1.620639
## 103    69/842  385/7577 2.987141e-05 5.727843e-03       1.612774
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              geneID
## 1                                                                                                                                                                                                                                   ENSG00000087258/ENSG00000095303/ENSG00000159251/ENSG00000185532/ENSG00000163681/ENSG00000120907/ENSG00000141052/ENSG00000149575/ENSG00000105974/ENSG00000109846/ENSG00000145362/ENSG00000164171/ENSG00000183023/ENSG00000213366/ENSG00000198947/ENSG00000140416/ENSG00000018625/ENSG00000171873/ENSG00000123096/ENSG00000198363/ENSG00000152661/ENSG00000138119/ENSG00000154229/ENSG00000182963/ENSG00000035403/ENSG00000151617/ENSG00000131730/ENSG00000163794/ENSG00000122786/ENSG00000197043/ENSG00000136842/ENSG00000124205/ENSG00000065534/ENSG00000136160/ENSG00000089250/ENSG00000180616/ENSG00000171596/ENSG00000143153/ENSG00000156113/ENSG00000198523/ENSG00000108405/ENSG00000105976/ENSG00000077157/ENSG00000075073/ENSG00000182718/ENSG00000073756/ENSG00000134769/ENSG00000050628/ENSG00000095637/ENSG00000072952/ENSG00000108823/ENSG00000163431/ENSG00000004776/ENSG00000133392/ENSG00000101605/ENSG00000107796/ENSG00000135046/ENSG00000183963/ENSG00000213949/ENSG00000266964/ENSG00000162004/ENSG00000138735/ENSG00000163017/ENSG00000130176/ENSG00000101335/ENSG00000136546/ENSG00000143632/ENSG00000118729/ENSG00000187848/ENSG00000170425/ENSG00000175084/ENSG00000168398/ENSG00000198467/ENSG00000132932/ENSG00000150594/ENSG00000114854/ENSG00000196091
## 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   ENSG00000105707/ENSG00000128714/ENSG00000163637/ENSG00000107485/ENSG00000019549/ENSG00000140285/ENSG00000066468/ENSG00000070193/ENSG00000169071/ENSG00000176697/ENSG00000189120/ENSG00000171791/ENSG00000151617/ENSG00000244405/ENSG00000142632/ENSG00000139174/ENSG00000135925/ENSG00000128713/ENSG00000105976/ENSG00000128573/ENSG00000155760/ENSG00000101144/ENSG00000074527/ENSG00000134245/ENSG00000126778/ENSG00000072163/ENSG00000140807/ENSG00000016082/ENSG00000064300/ENSG00000125845/ENSG00000106819/ENSG00000154342/ENSG00000147655/ENSG00000146374/ENSG00000158055/ENSG00000104313/ENSG00000005513/ENSG00000122691/ENSG00000170577/ENSG00000164932/ENSG00000008300/ENSG00000107984/ENSG00000125931/ENSG00000089225/ENSG00000105989/ENSG00000184058/ENSG00000166823
## 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   ENSG00000105971/ENSG00000159251/ENSG00000077092/ENSG00000141052/ENSG00000164976/ENSG00000165410/ENSG00000161055/ENSG00000121577/ENSG00000163069/ENSG00000122367/ENSG00000106462/ENSG00000132819/ENSG00000165996/ENSG00000183023/ENSG00000198947/ENSG00000157168/ENSG00000140416/ENSG00000163110/ENSG00000134775/ENSG00000171791/ENSG00000130513/ENSG00000164530/ENSG00000136842/ENSG00000089250/ENSG00000139174/ENSG00000162614/ENSG00000105976/ENSG00000197168/ENSG00000024422/ENSG00000072195/ENSG00000160539/ENSG00000112276/ENSG00000163431/ENSG00000133392/ENSG00000086991/ENSG00000115641/ENSG00000128591/ENSG00000182389/ENSG00000167244/ENSG00000125845/ENSG00000143632/ENSG00000154277/ENSG00000118729/ENSG00000187848/ENSG00000154342/ENSG00000170558/ENSG00000047597/ENSG00000115884/ENSG00000107984/ENSG00000089225/ENSG00000171345/ENSG00000184058/ENSG00000187957/ENSG00000166823/ENSG00000169245/ENSG00000142178
## 63  ENSG00000164764/ENSG00000095713/ENSG00000197565/ENSG00000154736/ENSG00000183287/ENSG00000166833/ENSG00000165072/ENSG00000069702/ENSG00000143196/ENSG00000112293/ENSG00000066468/ENSG00000124479/ENSG00000070193/ENSG00000142910/ENSG00000120885/ENSG00000065618/ENSG00000262655/ENSG00000115380/ENSG00000198732/ENSG00000167346/ENSG00000165078/ENSG00000100234/ENSG00000124749/ENSG00000064205/ENSG00000183798/ENSG00000185585/ENSG00000172638/ENSG00000196104/ENSG00000125848/ENSG00000205221/ENSG00000135925/ENSG00000140479/ENSG00000182718/ENSG00000196878/ENSG00000079215/ENSG00000140682/ENSG00000184347/ENSG00000159674/ENSG00000101144/ENSG00000130203/ENSG00000173376/ENSG00000074527/ENSG00000134245/ENSG00000131981/ENSG00000132702/ENSG00000163815/ENSG00000132470/ENSG00000151914/ENSG00000152583/ENSG00000187122/ENSG00000185070/ENSG00000106278/ENSG00000123500/ENSG00000157150/ENSG00000188372/ENSG00000116962/ENSG00000156218/ENSG00000065320/ENSG00000087303/ENSG00000106819/ENSG00000106809/ENSG00000132561/ENSG00000124107/ENSG00000132386/ENSG00000154342/ENSG00000119699/ENSG00000156103/ENSG00000091986/ENSG00000113296/ENSG00000145113/ENSG00000108679/ENSG00000164932/ENSG00000187720/ENSG00000166670/ENSG00000100985/ENSG00000011465/ENSG00000100473/ENSG00000138829/ENSG00000112280/ENSG00000150893/ENSG00000105664/ENSG00000105989/ENSG00000167772/ENSG00000197614/ENSG00000160180/ENSG00000049089/ENSG00000133048/ENSG00000185303/ENSG00000034971/ENSG00000163810/ENSG00000139219
## 64                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  ENSG00000159251/ENSG00000120907/ENSG00000263155/ENSG00000165410/ENSG00000122367/ENSG00000121361/ENSG00000073712/ENSG00000144935/ENSG00000109846/ENSG00000145362/ENSG00000185567/ENSG00000183023/ENSG00000198947/ENSG00000140416/ENSG00000163110/ENSG00000134775/ENSG00000197321/ENSG00000152661/ENSG00000147166/ENSG00000162520/ENSG00000197361/ENSG00000058668/ENSG00000035403/ENSG00000129116/ENSG00000122786/ENSG00000136842/ENSG00000240771/ENSG00000089250/ENSG00000162614/ENSG00000156804/ENSG00000149596/ENSG00000077157/ENSG00000069431/ENSG00000154330/ENSG00000172403/ENSG00000182253/ENSG00000163431/ENSG00000133392/ENSG00000176749/ENSG00000101605/ENSG00000115641/ENSG00000151914/ENSG00000107796/ENSG00000157150/ENSG00000128591/ENSG00000101335/ENSG00000143632/ENSG00000157388/ENSG00000118729/ENSG00000175084/ENSG00000198467/ENSG00000164591/ENSG00000171345/ENSG00000114854/ENSG00000196091
## 65                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  ENSG00000163681/ENSG00000162989/ENSG00000120907/ENSG00000121577/ENSG00000163069/ENSG00000142583/ENSG00000120457/ENSG00000121361/ENSG00000145362/ENSG00000185567/ENSG00000183023/ENSG00000127990/ENSG00000198947/ENSG00000018625/ENSG00000123096/ENSG00000162520/ENSG00000058668/ENSG00000151617/ENSG00000158445/ENSG00000197043/ENSG00000089250/ENSG00000181856/ENSG00000143153/ENSG00000182718/ENSG00000134769/ENSG00000069431/ENSG00000154330/ENSG00000112276/ENSG00000108823/ENSG00000136425/ENSG00000135046/ENSG00000266964/ENSG00000162004/ENSG00000128591/ENSG00000157388/ENSG00000119699/ENSG00000175084/ENSG00000171345
## 79                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  ENSG00000188488/ENSG00000114631/ENSG00000119630/ENSG00000166833/ENSG00000069702/ENSG00000133800/ENSG00000140285/ENSG00000066468/ENSG00000070193/ENSG00000138685/ENSG00000112902/ENSG00000204381/ENSG00000127418/ENSG00000064205/ENSG00000196104/ENSG00000205221/ENSG00000118257/ENSG00000140479/ENSG00000113657/ENSG00000002726/ENSG00000184347/ENSG00000101670/ENSG00000101144/ENSG00000130203/ENSG00000137462/ENSG00000173376/ENSG00000132702/ENSG00000000971/ENSG00000072571/ENSG00000163815/ENSG00000187122/ENSG00000124875/ENSG00000106809/ENSG00000184613/ENSG00000147655/ENSG00000146374/ENSG00000091986/ENSG00000113296/ENSG00000011465/ENSG00000105664/ENSG00000169248/ENSG00000169245/ENSG00000012223
## 80                                                                                                                                                                                                                                                                                                                                  ENSG00000157551/ENSG00000105707/ENSG00000130529/ENSG00000162989/ENSG00000131620/ENSG00000171714/ENSG00000171227/ENSG00000169282/ENSG00000172005/ENSG00000115041/ENSG00000177119/ENSG00000149575/ENSG00000185760/ENSG00000150625/ENSG00000137672/ENSG00000120457/ENSG00000121361/ENSG00000137726/ENSG00000144935/ENSG00000159212/ENSG00000225697/ENSG00000185052/ENSG00000145936/ENSG00000107130/ENSG00000152661/ENSG00000164647/ENSG00000171791/ENSG00000182963/ENSG00000158445/ENSG00000151572/ENSG00000197043/ENSG00000133107/ENSG00000169504/ENSG00000156113/ENSG00000088836/ENSG00000149596/ENSG00000108405/ENSG00000069431/ENSG00000188910/ENSG00000083454/ENSG00000144481/ENSG00000157445/ENSG00000138449/ENSG00000169583/ENSG00000175538/ENSG00000171303/ENSG00000150995/ENSG00000141469/ENSG00000188372/ENSG00000169562/ENSG00000266964/ENSG00000182389/ENSG00000136546/ENSG00000157388/ENSG00000187848/ENSG00000099822/ENSG00000111319/ENSG00000213199/ENSG00000184156/ENSG00000166828/ENSG00000094755/ENSG00000162572/ENSG00000168447/ENSG00000171126/ENSG00000166206/ENSG00000120903/ENSG00000142185/ENSG00000146205/ENSG00000183960/ENSG00000001626/ENSG00000198785
## 81                                                                                                                                                                                                                                                                                                                                  ENSG00000157551/ENSG00000105707/ENSG00000130529/ENSG00000162989/ENSG00000131620/ENSG00000171714/ENSG00000171227/ENSG00000169282/ENSG00000172005/ENSG00000115041/ENSG00000177119/ENSG00000149575/ENSG00000185760/ENSG00000150625/ENSG00000137672/ENSG00000120457/ENSG00000121361/ENSG00000137726/ENSG00000144935/ENSG00000159212/ENSG00000225697/ENSG00000185052/ENSG00000145936/ENSG00000107130/ENSG00000152661/ENSG00000164647/ENSG00000171791/ENSG00000182963/ENSG00000158445/ENSG00000151572/ENSG00000197043/ENSG00000133107/ENSG00000169504/ENSG00000156113/ENSG00000088836/ENSG00000149596/ENSG00000108405/ENSG00000069431/ENSG00000188910/ENSG00000083454/ENSG00000144481/ENSG00000157445/ENSG00000138449/ENSG00000169583/ENSG00000175538/ENSG00000171303/ENSG00000150995/ENSG00000141469/ENSG00000188372/ENSG00000169562/ENSG00000266964/ENSG00000182389/ENSG00000136546/ENSG00000157388/ENSG00000187848/ENSG00000099822/ENSG00000111319/ENSG00000213199/ENSG00000184156/ENSG00000166828/ENSG00000094755/ENSG00000162572/ENSG00000168447/ENSG00000171126/ENSG00000166206/ENSG00000120903/ENSG00000142185/ENSG00000146205/ENSG00000183960/ENSG00000001626/ENSG00000198785
## 91                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  ENSG00000043591/ENSG00000077943/ENSG00000105855/ENSG00000108823/ENSG00000114854/ENSG00000115221/ENSG00000119699/ENSG00000132470/ENSG00000140416/ENSG00000144668/ENSG00000157388/ENSG00000157445/ENSG00000159251/ENSG00000161638/ENSG00000163069/ENSG00000164171/ENSG00000173175/ENSG00000175084/ENSG00000182389/ENSG00000183023/ENSG00000198467/ENSG00000198523/ENSG00000198947/ENSG00000213949/ENSG00000259207
## 92                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  ENSG00000077943/ENSG00000105855/ENSG00000108823/ENSG00000114854/ENSG00000115221/ENSG00000119699/ENSG00000132470/ENSG00000140416/ENSG00000144668/ENSG00000157388/ENSG00000157445/ENSG00000159251/ENSG00000161638/ENSG00000163069/ENSG00000164171/ENSG00000175084/ENSG00000182389/ENSG00000183023/ENSG00000198467/ENSG00000198947/ENSG00000213949/ENSG00000259207
## 93                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  ENSG00000077943/ENSG00000105855/ENSG00000108823/ENSG00000115221/ENSG00000132470/ENSG00000144668/ENSG00000152284/ENSG00000152661/ENSG00000157388/ENSG00000157445/ENSG00000161638/ENSG00000163069/ENSG00000164171/ENSG00000170558/ENSG00000175084/ENSG00000182389/ENSG00000183023/ENSG00000198947/ENSG00000213949/ENSG00000259207
## 101                                                                                                                                                                                                                                                 ENSG00000019549/ENSG00000036672/ENSG00000073756/ENSG00000075213/ENSG00000076706/ENSG00000077092/ENSG00000084207/ENSG00000089685/ENSG00000100985/ENSG00000101144/ENSG00000101955/ENSG00000103175/ENSG00000105974/ENSG00000105976/ENSG00000106462/ENSG00000106483/ENSG00000109819/ENSG00000111837/ENSG00000114200/ENSG00000115884/ENSG00000115963/ENSG00000118971/ENSG00000122691/ENSG00000124225/ENSG00000125257/ENSG00000125845/ENSG00000125968/ENSG00000130203/ENSG00000130513/ENSG00000131981/ENSG00000132470/ENSG00000134184/ENSG00000134215/ENSG00000134602/ENSG00000136689/ENSG00000142515/ENSG00000142627/ENSG00000143546/ENSG00000144481/ENSG00000145113/ENSG00000145675/ENSG00000147889/ENSG00000148773/ENSG00000151632/ENSG00000156076/ENSG00000156103/ENSG00000156113/ENSG00000156970/ENSG00000159184/ENSG00000159263/ENSG00000161638/ENSG00000163220/ENSG00000164171/ENSG00000166851/ENSG00000167244/ENSG00000167346/ENSG00000167653/ENSG00000167751/ENSG00000169245/ENSG00000169710/ENSG00000169862/ENSG00000170962/ENSG00000171791/ENSG00000172005/ENSG00000176749/ENSG00000182718/ENSG00000185630/ENSG00000187210/ENSG00000196549/ENSG00000196924/ENSG00000197588/ENSG00000212993/ENSG00000225937/ENSG00000242110/ENSG00000259207/ENSG00000277893
## 102                                                                                                                                                                                                                                                 ENSG00000019549/ENSG00000036672/ENSG00000073756/ENSG00000075213/ENSG00000076706/ENSG00000077092/ENSG00000084207/ENSG00000089685/ENSG00000100985/ENSG00000101144/ENSG00000101955/ENSG00000103175/ENSG00000105974/ENSG00000105976/ENSG00000106462/ENSG00000106483/ENSG00000109819/ENSG00000111837/ENSG00000114200/ENSG00000115884/ENSG00000115963/ENSG00000118971/ENSG00000122691/ENSG00000124225/ENSG00000125257/ENSG00000125845/ENSG00000125968/ENSG00000130203/ENSG00000130513/ENSG00000131981/ENSG00000132470/ENSG00000134184/ENSG00000134215/ENSG00000134602/ENSG00000136689/ENSG00000142515/ENSG00000142627/ENSG00000143546/ENSG00000144481/ENSG00000145113/ENSG00000145675/ENSG00000147889/ENSG00000148773/ENSG00000151632/ENSG00000156076/ENSG00000156103/ENSG00000156113/ENSG00000156970/ENSG00000159184/ENSG00000159263/ENSG00000161638/ENSG00000163220/ENSG00000164171/ENSG00000166851/ENSG00000167244/ENSG00000167346/ENSG00000167653/ENSG00000167751/ENSG00000169245/ENSG00000169710/ENSG00000169862/ENSG00000170962/ENSG00000171791/ENSG00000172005/ENSG00000176749/ENSG00000182718/ENSG00000185630/ENSG00000187210/ENSG00000196549/ENSG00000196924/ENSG00000197588/ENSG00000212993/ENSG00000225937/ENSG00000242110/ENSG00000259207/ENSG00000277893
## 103                                                                                                                                                                                                                                                                                                                                                                 ENSG00000007062/ENSG00000010704/ENSG00000013619/ENSG00000018236/ENSG00000022267/ENSG00000035403/ENSG00000043591/ENSG00000048740/ENSG00000049089/ENSG00000069431/ENSG00000069535/ENSG00000089250/ENSG00000089685/ENSG00000100985/ENSG00000101605/ENSG00000102024/ENSG00000104879/ENSG00000105974/ENSG00000105976/ENSG00000108823/ENSG00000109846/ENSG00000112280/ENSG00000112319/ENSG00000113580/ENSG00000114115/ENSG00000114854/ENSG00000120907/ENSG00000122367/ENSG00000123096/ENSG00000128591/ENSG00000130203/ENSG00000134363/ENSG00000134769/ENSG00000135218/ENSG00000136160/ENSG00000137077/ENSG00000137462/ENSG00000140416/ENSG00000143546/ENSG00000143632/ENSG00000147889/ENSG00000148346/ENSG00000149294/ENSG00000149596/ENSG00000150995/ENSG00000151617/ENSG00000152661/ENSG00000154229/ENSG00000158887/ENSG00000159251/ENSG00000159899/ENSG00000162520/ENSG00000163069/ENSG00000163071/ENSG00000164342/ENSG00000165410/ENSG00000169245/ENSG00000170558/ENSG00000171714/ENSG00000171791/ENSG00000175084/ENSG00000176697/ENSG00000177469/ENSG00000182253/ENSG00000196091/ENSG00000198467/ENSG00000198523/ENSG00000198947/ENSG00000280987
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          geneSymbol
## 1                                                                                                                GNAO1/PTGS1/ACTC1/PRKG1/SLMAP/ADRA1A/MYOCD/SCN2B/CAV1/CRYAB/ANK2/ITGA2/SLC8A1/GSTM2/DMD/TPM1/ATP1A2/ADRA1D/SSPN/ASPH/GJA1/MYOF/PRKCA/GJC1/VCL/EDNRA/CKMT2/UCN/CALD1/ANXA6/TMOD1/EDN3/MYLK/EDNRB/NOS1/SSTR2/NMUR1/ATP1B1/KCNMA1/PLN/P2RX1/MET/PPP1R12B/TACR2/ANXA2/PTGS2/DTNA/PTGER3/SORBS1/MRVI1/SGCA/LMOD1/HSPB6/MYH11/MYOM1/ACTA2/ANXA1/SMTN/ITGA1/FXYD1/CCDC78/PDE5A/ACTG2/CNN1/MYL9/SCN7A/ACTA1/CASQ2/P2RX2/ADORA2B/DES/BDKRB2/TPM2/ATP8A2/ADRA2A/TNNC1/MYBPC1
## 2                                                                                                                                                                                                                                                                                                    HPN/HOXD13/PRICKLE2/GATA3/SNAI2/FGF7/FGFR2/FGF10/ROR2/BDNF/SP6/BCL2/EDNRA/ETV5/ARHGEF19/PRICKLE1/WNT10A/HOXD11/MET/FOXP2/FZD7/BMP7/NTN4/WNT2B/SIX1/LIMS2/NKD1/ISL1/NGFR/BMP2/ASPN/WNT3A/RSPO2/RSPO3/GRHL3/EYA1/SOX8/TWIST1/SIX2/CTHRC1/CELSR3/DKK1/CITED1/TBX5/WNT2/TBX1/MESP1
## 3                                                                                                                                                                                                                                                         CAV2/ACTC1/RARB/MYOCD/KIAA1161/CFL2/SCGB3A1/POPDC2/SGCB/LDB3/EZH2/RBM38/HACD1/SLC8A1/DMD/NRG1/TPM1/PDLIM5/FHOD3/BCL2/GDF15/PI16/TMOD1/NOS1/PRICKLE1/NEXN/MET/NEK5/EHD2/SPEG/PLPP7/BVES/LMOD1/MYH11/NOX4/FHL2/FLNC/CACNB4/IGF2/BMP2/ACTA1/UCHL1/CASQ2/P2RX2/WNT3A/CDH2/XK/SDC1/DKK1/TBX5/KRT19/TBX1/DNER/MESP1/CXCL10/SIK1
## 63  SBSPON/CRTAC1/COL4A6/ADAMTS5/CCBE1/NAV2/MAMDC2/TGFBR3/DPT/GPLD1/FGFR2/NDP/FGF10/TINAGL1/CLU/COL17A1/SPON1/EFEMP1/SMOC1/MMP26/CPA6/TIMP3/COL21A1/WISP2/EMILIN3/OLFML2A/EFEMP2/SPOCK3/FLRT3/VIT/WNT10A/PCSK6/ANXA2/LAMB3/SLC1A3/TGFB1I1/SLIT3/SPON2/BMP7/APOE/NDNF/NTN4/WNT2B/LGALS3/HAPLN2/CLEC3B/ITGB4/DST/SPARCL1/SLIT1/FLRT2/PTPRZ1/COL10A1/TIMP4/ZP3/NID1/ADAMTSL3/NTN1/NID2/ASPN/OGN/MATN2/SLPI/SERPINF1/WNT3A/TGFB3/MMP16/CCDC80/THBS4/MUC4/LGALS3BP/CTHRC1/THSD4/MMP10/MMP9/DCN/COCH/FBN2/COL9A1/FREM2/COMP/WNT2/ANGPTL4/MFAP5/TFF3/COL9A2/CHI3L1/SFTPA2/MYOC/TGM4/COL2A1
## 64                                                                                                                                                                                                                                          ACTC1/ADRA1A/MYZAP/CFL2/LDB3/KCNJ8/FERMT2/TRPC1/CRYAB/ANK2/AHNAK2/SLC8A1/DMD/TPM1/PDLIM5/FHOD3/SVIL/GJA1/ITGB1BP2/SYNC/FBXL22/ATP2B4/VCL/PALLD/CALD1/TMOD1/ARHGEF25/NOS1/NEXN/FBXO32/JPH2/PPP1R12B/ABCC9/PGM5/SYNPO2/SYNM/LMOD1/MYH11/CDK5R1/MYOM1/FHL2/DST/ACTA2/TIMP4/FLNC/MYL9/ACTA1/CACNA1D/CASQ2/DES/TPM2/MYOZ3/KRT19/TNNC1/MYBPC1
## 65                                                                                                                                                                                                                                                                                                                                                  SLMAP/KCNJ3/ADRA1A/POPDC2/SGCB/SLC2A5/KCNJ5/KCNJ8/ANK2/AHNAK2/SLC8A1/SGCE/DMD/ATP1A2/SSPN/SYNC/ATP2B4/EDNRA/KCNB1/ANXA6/NOS1/SLC2A4/ATP1B1/ANXA2/DTNA/ABCC9/PGM5/BVES/SGCA/CIB2/ANXA1/FXYD1/CCDC78/FLNC/CACNA1D/TGFB3/DES/KRT19
## 79                                                                                                                                                                                                                                                                                                                           SERPINA5/PODXL2/PGF/NAV2/TGFBR3/LYVE1/FGF7/FGFR2/FGF10/FGF2/SEMA5A/LAYN/FGFRL1/WISP2/SPOCK3/VIT/NRP2/PCSK6/DPYSL3/AOC1/SLIT3/LIPG/BMP7/APOE/TLR2/NDNF/HAPLN2/CFH/HMMR/CLEC3B/SLIT1/CXCL6/OGN/NELL2/RSPO2/RSPO3/CCDC80/THBS4/DCN/COMP/CXCL11/CXCL10/LTF
## 80                                                                                                                              KCNJ15/HPN/TRPM4/KCNJ3/ANO1/ANO5/TMEM37/KCNAB1/MAL/KCNIP3/ANO6/SCN2B/KCNQ5/GPM6A/TRPC6/KCNJ5/KCNJ8/FXYD6/TRPC1/CLIC6/SLC26A6/SLC24A3/KCNMB1/NCS1/GJA1/STEAP1/BCL2/GJC1/KCNB1/ANO4/ANXA6/TRPC4/CLIC4/KCNMA1/SLC4A11/JPH2/P2RX1/ABCC9/GJB3/P2RX5/TRPM8/CACNA2D3/SLC40A1/CLIC3/KCNE3/KCNK3/ITPR1/SLC14A1/ZP3/GJB1/FXYD1/CACNB4/SCN7A/CACNA1D/P2RX2/HCN2/SCNN1A/ASIC3/KCNQ3/SCNN1G/GABRP/SCNN1D/SCNN1B/KCNG3/GABRB3/CHRNA2/TRPM2/ANO7/KCNH8/CFTR/GRIN3A
## 81                                                                                                                              KCNJ15/HPN/TRPM4/KCNJ3/ANO1/ANO5/TMEM37/KCNAB1/MAL/KCNIP3/ANO6/SCN2B/KCNQ5/GPM6A/TRPC6/KCNJ5/KCNJ8/FXYD6/TRPC1/CLIC6/SLC26A6/SLC24A3/KCNMB1/NCS1/GJA1/STEAP1/BCL2/GJC1/KCNB1/ANO4/ANXA6/TRPC4/CLIC4/KCNMA1/SLC4A11/JPH2/P2RX1/ABCC9/GJB3/P2RX5/TRPM8/CACNA2D3/SLC40A1/CLIC3/KCNE3/KCNK3/ITPR1/SLC14A1/ZP3/GJB1/FXYD1/CACNB4/SCN7A/CACNA1D/P2RX2/HCN2/SCNN1A/ASIC3/KCNQ3/SCNN1G/GABRP/SCNN1D/SCNN1B/KCNG3/GABRB3/CHRNA2/TRPM2/ANO7/KCNH8/CFTR/GRIN3A
## 91                                                                                                                                                                                                                                                                                                                                                                                                                               ADRB1/ITGA8/ITGB8/SGCA/TNNC1/ITGB6/TGFB3/ITGB4/TPM1/ITGA9/CACNA1D/CACNA2D3/ACTC1/ITGA5/SGCB/ITGA2/ADCY5/DES/CACNB4/SLC8A1/TPM2/PLN/DMD/ITGA1/ITGB3
## 92                                                                                                                                                                                                                                                                                                                                                                                                                                               ITGA8/ITGB8/SGCA/TNNC1/ITGB6/TGFB3/ITGB4/TPM1/ITGA9/CACNA1D/CACNA2D3/ACTC1/ITGA5/SGCB/ITGA2/DES/CACNB4/SLC8A1/TPM2/DMD/ITGA1/ITGB3
## 93                                                                                                                                                                                                                                                                                                                                                                                                                                                          ITGA8/ITGB8/SGCA/ITGB6/ITGB4/ITGA9/TCF7L1/GJA1/CACNA1D/CACNA2D3/ITGA5/SGCB/ITGA2/CDH2/DES/CACNB4/SLC8A1/DMD/ITGA1/ITGB3
## 101                                                                                                                          SNAI2/USP2/PTGS2/SEMA3A/MCAM/RARB/GSTP1/BIRC5/MMP9/BMP7/SRPX/WFDC1/CAV1/MET/EZH2/SFRP4/PPARGC1A/MAK/BCHE/SDC1/RND3/CCND2/TWIST1/PMEPA1/ABCC4/BMP2/ID1/APOE/GDF15/LGALS3/ITGB4/GSTM1/VAV3/STK26/IL1RN/KLK3/EPHA2/S100A8/TRPM8/MUC4/PIK3R1/CDKN2A/MKI67/AKR1C2/WIF1/MMP16/KCNMA1/BUB1B/HOXB13/SIM2/ITGA5/S100A9/ITGA2/PLK1/IGF2/MMP26/PSCA/KLK2/CXCL10/FASN/CTNND2/PDGFD/BCL2/MAL/CDK5R1/ANXA2/PBX1/GCNT1/MME/FLNA/KLKP1/POU5F1B/PCA3/AMACR/ITGB3/SRD5A2
## 102                                                                                                                          SNAI2/USP2/PTGS2/SEMA3A/MCAM/RARB/GSTP1/BIRC5/MMP9/BMP7/SRPX/WFDC1/CAV1/MET/EZH2/SFRP4/PPARGC1A/MAK/BCHE/SDC1/RND3/CCND2/TWIST1/PMEPA1/ABCC4/BMP2/ID1/APOE/GDF15/LGALS3/ITGB4/GSTM1/VAV3/STK26/IL1RN/KLK3/EPHA2/S100A8/TRPM8/MUC4/PIK3R1/CDKN2A/MKI67/AKR1C2/WIF1/MMP16/KCNMA1/BUB1B/HOXB13/SIM2/ITGA5/S100A9/ITGA2/PLK1/IGF2/MMP26/PSCA/KLK2/CXCL10/FASN/CTNND2/PDGFD/BCL2/MAL/CDK5R1/ANXA2/PBX1/GCNT1/MME/FLNA/KLKP1/POU5F1B/PCA3/AMACR/ITGB3/SRD5A2
## 103                                                                                                                                                                                         PROM1/HFE/MAMLD1/CNTN1/FHL1/VCL/ADRB1/CELF2/COL9A2/ABCC9/MAOB/NOS1/BIRC5/MMP9/MYOM1/PLS3/CKM/CAV1/MET/SGCA/CRYAB/COL9A1/EYA4/NR3C1/RBP1/TNNC1/ADRA1A/LDB3/SSPN/FLNC/APOE/FST/DTNA/CD36/EDNRB/CCL21/TLR2/TPM1/S100A8/ACTA1/CDKN2A/LCN2/NCAM1/JPH2/ITPR1/EDNRA/GJA1/PRKCA/MPZ/ACTC1/NPR2/SYNC/SGCB/SPATA18/TLR3/CFL2/CXCL10/CDH2/ANO5/BCL2/DES/BDNF/CAVIN1/SYNM/MYBPC1/TPM2/PLN/DMD/MATR3
##     Category
## 1      GO_BP
## 2      GO_BP
## 3      GO_BP
## 63     GO_CC
## 64     GO_CC
## 65     GO_CC
## 79     GO_MF
## 80     GO_MF
## 81     GO_MF
## 91      KEGG
## 92      KEGG
## 93      KEGG
## 101       DO
## 102       DO
## 103       DO

8.2 Enrichment visualization

The output generated by gdcEnrichAnalysis() can be used for visualization in the gdcEnrichPlot() function by specifying type,category and numTerms arguments.

8.2.1 GO barplot

gdcEnrichPlot(enrichOutput, type = 'bar', category = 'GO', num.terms = 10)

8.2.2 GO bubble plot

gdcEnrichPlot(enrichOutput, type='bubble', category='GO', num.terms = 10)

8.2.3 KEGG/DO barplot

gdcEnrichPlot(enrichment = enrichOutput, 
              type       = 'bar', 
              category   = 'KEGG', 
              bar.color  = 'chocolate1', 
              num.terms  = 20)
gdcEnrichPlot(enrichment = enrichOutput, 
              type       = 'bar', 
              category   = 'DO', 
              bar.color  = 'dodgerblue', 
              num.terms  = 20)

8.2.4 KEGG/DO bubble plot

gdcEnrichPlot(=enrichOutput, category='KEGG',type = 'bubble', num.terms = 20)
gdcEnrichPlot(enrichOutput, category='DO',type = 'bubble', num.terms = 20)

8.2.5 Pathview

Users can visualize a pathway map with pathview() function in the pathview(Luo and Brouwer 2013) package. It displays related many-genes-to-many-terms on 2-D view, shows by genes on BioCarta & KEGG pathway maps. Gradient colors can be used to indicate if genes are up-regulated or down-regulated.

library(pathview)
deg <- deALL$logFC
names(deg) <- rownames(deALL)

hsa04022 <- pathview(gene.data   = deg,
                     pathway.id  = "hsa04022",
                     species     = "hsa",
                     gene.idtype = 'ENSEMBL',
                     limit       = list(gene=max(abs(geneList)), cpd=1))

8.2.6 View pathway maps on a local webpage by shinyPathview

shinyPathview() allows users view and download pathways of interests by simply selecting the pathway terms on a local webpage.

pathways <- as.character(enrichOutput$Terms[enrichOutput$Category=='KEGG'])

shinyPathview(deg, pathways = pathways, directory = 'pathview')

9 sessionInfo

sessionInfo()
## R version 3.3.1 (2016-06-21)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.1 LTS
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] GDCRNATools_0.99.0
## 
## loaded via a namespace (and not attached):
##   [1] colorspace_1.3-2           rjson_0.2.15              
##   [3] rprojroot_1.2              qvalue_2.4.2              
##   [5] htmlTable_1.9              XVector_0.12.1            
##   [7] GenomicRanges_1.24.3       base64enc_0.1-3           
##   [9] ggpubr_0.1.6               topGO_2.24.0              
##  [11] bit64_0.9-7                AnnotationDbi_1.34.4      
##  [13] splines_3.3.1              mnormt_1.5-5              
##  [15] GOSemSim_1.30.3            geneplotter_1.50.0        
##  [17] knitr_1.17                 pathview_1.12.0           
##  [19] Formula_1.2-2              jsonlite_1.5              
##  [21] km.ci_0.5-2                broom_0.4.2               
##  [23] annotate_1.50.1            cluster_2.0.6             
##  [25] GO.db_3.3.0                png_0.1-7                 
##  [27] graph_1.50.0               shiny_1.0.5               
##  [29] httr_1.3.1                 backports_1.1.1           
##  [31] assertthat_0.2.0           Matrix_1.2-11             
##  [33] lazyeval_0.2.1             limma_3.28.21             
##  [35] acepack_1.4.1              htmltools_0.3.6           
##  [37] tools_3.3.1                bindrcpp_0.2              
##  [39] igraph_1.1.2               gtable_0.2.0              
##  [41] glue_1.2.0                 reshape2_1.4.2            
##  [43] DO.db_2.9                  dplyr_0.7.4               
##  [45] Rcpp_0.12.13               Biobase_2.32.0            
##  [47] Biostrings_2.40.2          gdata_2.18.0              
##  [49] nlme_3.1-131               psych_1.7.8               
##  [51] stringr_1.2.0              mime_0.5                  
##  [53] clusterProfiler_3.0.5      gtools_3.5.0              
##  [55] XML_3.98-1.9               DOSE_2.10.7               
##  [57] org.Hs.eg.db_3.3.0         edgeR_3.14.0              
##  [59] zoo_1.8-0                  zlibbioc_1.18.0           
##  [61] scales_0.5.0               parallel_3.3.1            
##  [63] SummarizedExperiment_1.2.3 KEGGgraph_1.30.0          
##  [65] SparseM_1.77               RColorBrewer_1.1-2        
##  [67] yaml_2.1.14                memoise_1.1.0             
##  [69] gridExtra_2.3              KMsurv_0.1-5              
##  [71] ggplot2_2.2.1              biomaRt_2.28.0            
##  [73] rpart_4.1-11               latticeExtra_0.6-28       
##  [75] stringi_1.1.5              RSQLite_2.0               
##  [77] genefilter_1.54.2          S4Vectors_0.10.3          
##  [79] checkmate_1.8.5            caTools_1.17.1            
##  [81] BiocGenerics_0.18.0        BiocParallel_1.6.6        
##  [83] GenomeInfoDb_1.8.7         rlang_0.1.4               
##  [85] pkgconfig_2.0.1            matrixStats_0.52.2        
##  [87] bitops_1.0-6               evaluate_0.10.1           
##  [89] lattice_0.20-35            purrr_0.2.4               
##  [91] bindr_0.1                  labeling_0.3              
##  [93] cmprsk_2.2-7               htmlwidgets_0.9           
##  [95] tidyselect_0.2.3           bit_1.1-12                
##  [97] GSEABase_1.34.1            plyr_1.8.4                
##  [99] magrittr_1.5               DESeq2_1.12.4             
## [101] R6_2.2.2                   IRanges_2.6.1             
## [103] gplots_3.0.1               Hmisc_4.0-3               
## [105] DBI_0.7                    foreign_0.8-69            
## [107] prettydoc_0.2.0            survival_2.41-3           
## [109] KEGGREST_1.12.3            RCurl_1.95-4.8            
## [111] nnet_7.3-12                tibble_1.3.4              
## [113] survMisc_0.5.4             KernSmooth_2.23-15        
## [115] rmarkdown_1.7              locfit_1.5-9.1            
## [117] grid_3.3.1                 data.table_1.10.4-3       
## [119] blob_1.1.0                 Rgraphviz_2.16.0          
## [121] digest_0.6.12              xtable_1.8-2              
## [123] tidyr_0.7.2                httpuv_1.3.5              
## [125] stats4_3.3.1               munsell_0.4.3             
## [127] survminer_0.4.0

References

Chou, Chih-Hung, Sirjana Shrestha, Chi-Dung Yang, Nai-Wen Chang, Yu-Ling Lin, Kuang-Wen Liao, Wei-Chi Huang, et al. 2017. “MiRTarBase Update 2018: A Resource for Experimentally Validated MicroRNA-Target Interactions.” Nucleic Acids Research, November, gkx1067–gkx1067. doi:10.1093/nar/gkx1067.

Colaprico, Antonio, Tiago C. Silva, Catharina Olsen, Luciano Garofano, Claudia Cava, Davide Garolini, Thais S. Sabedot, et al. 2016. “TCGAbiolinks: An R/Bioconductor Package for Integrative Analysis of TCGA Data.” Nucleic Acids Research 44 (8): e71. doi:10.1093/nar/gkv1507.

Furi’o-Tar’i, Pedro, Sonia Tarazona, Toni Gabald’on, Anton J. Enright, and Ana Conesa. 2016. “SpongeScan: A Web for Detecting MicroRNA Binding Elements in LncRNA Sequences.” Nucleic Acids Research 44 (Web Server issue): W176–W180. doi:10.1093/nar/gkw443.

Jeggari, Ashwini, Debora S Marks, and Erik Larsson. 2012. “MiRcode: A Map of Putative MicroRNA Target Sites in the Long Non-Coding Transcriptome.” Bioinformatics 28 (15): 2062–3. doi:10.1093/bioinformatics/bts344.

Langfelder, Peter, and Steve Horvath. 2008. “WGCNA: An R Package for Weighted Correlation Network Analysis.” BMC Bioinformatics 9 (December): 559. doi:10.1186/1471-2105-9-559.

Law, Charity W., Yunshun Chen, Wei Shi, and Gordon K. Smyth. 2014. “Voom: Precision Weights Unlock Linear Model Analysis Tools for RNA-Seq Read Counts.” Genome Biology 15 (February): R29. doi:10.1186/gb-2014-15-2-r29.

Li, Jun-Hao, Shun Liu, Hui Zhou, Liang-Hu Qu, and Jian-Hua Yang. 2014. “StarBase V2.0: Decoding MiRNA-CeRNA, MiRNA-NcRNA and Protein–RNA Interaction Networks from Large-Scale CLIP-Seq Data.” Nucleic Acids Research 42 (Database issue): D92–D97. doi:10.1093/nar/gkt1248.

Love, Michael I., Wolfgang Huber, and Simon Anders. 2014. “Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2.” Genome Biology 15 (December): 550. doi:10.1186/s13059-014-0550-8.

Luo, Weijun, and Cory Brouwer. 2013. “Pathview: An R/Bioconductor Package for Pathway-Based Data Integration and Visualization.” Bioinformatics 29 (14): 1830–1. doi:10.1093/bioinformatics/btt285.

Paci, Paola, Teresa Colombo, and Lorenzo Farina. 2014. “Computational Analysis Identifies a Sponge Interaction Network Between Long Non-Coding RNAs and Messenger RNAs in Human Breast Cancer.” BMC Systems Biology 8 (July): 83. doi:10.1186/1752-0509-8-83.

Ritchie, Matthew E., Belinda Phipson, Di Wu, Yifang Hu, Charity W. Law, Wei Shi, and Gordon K. Smyth. 2015. “Limma Powers Differential Expression Analyses for RNA-Sequencing and Microarray Studies.” Nucleic Acids Research 43 (7): e47. doi:10.1093/nar/gkv007.

Robinson, Mark D., and Alicia Oshlack. 2010. “A Scaling Normalization Method for Differential Expression Analysis of RNA-Seq Data.” Genome Biology 11 (March): R25. doi:10.1186/gb-2010-11-3-r25.

Robinson, Mark D., Davis J. McCarthy, and Gordon K. Smyth. 2010. “EdgeR: A Bioconductor Package for Differential Expression Analysis of Digital Gene Expression Data.” Bioinformatics 26 (1): 139–40. doi:10.1093/bioinformatics/btp616.

Yu, Guangchuang, Li-Gen Wang, Yanyan Han, and Qing-Yu He. 2012. “ClusterProfiler: An R Package for Comparing Biological Themes Among Gene Clusters.” OMICS : A Journal of Integrative Biology 16 (5): 284–87. doi:10.1089/omi.2011.0118.

Yu, Guangchuang, Li-Gen Wang, Guang-Rong Yan, and Qing-Yu He. 2015. “DOSE: An R/Bioconductor Package for Disease Ontology Semantic and Enrichment Analysis.” Bioinformatics 31 (4): 608–9. doi:10.1093/bioinformatics/btu684.

Zhu, Yitan, Peng Qiu, and Yuan Ji. 2014. “TCGA-Assembler: An Open-Source Pipeline for TCGA Data Downloading, Assembling, and Processing.” Nature Methods 11 (6): 599–600. doi:10.1038/nmeth.2956.